Designing Polyoxometalate Thin Films on Carbon Nanomaterials for Pseudocapacitive Electrodes
Bibliographic record
Abstract
Multi-Walled Carbon Nanotubes (MWCNTs) modified with mixtures of polyoxometalate (POM) ions were investigated as pseudocapacitive electrodes for electrochemical capacitors. POMs undergo fast reversible multi-electron transfer reactions, and different POM molecules such as PMo 12 O 40 3- (PMo 12 ) and PW 12 O 40 3- (PW 12 ) demonstrate characteristic electrochemical activity related to their chemical composition. However, we have discovered that when these molecules are combined in aqueous solutions, the resulting mixture shows unique electrochemical properties leading to cyclic voltammograms (CVs) very different from those of the constituent molecules. Further analyses revealed that these POM ions do not just mix physically in aqueous solutions, but instead react spontaneously to form PMo 12-x W x mixed addenda chemistries. By simply mixing PMo 12 and PW 12 in different ratios, a variety of mixed addenda ions, each with unique electrochemical properties, can be easily synthesized. This control over POM redox behavior afforded by the novel mixed addenda synthesis is used along with layer-by-layer (LbL) deposition to design molecular coatings that demonstrate desired pseudocapacitive characteristics. The best performance was achieved with a coating that superimposed a 3:1 PMo 12 O 40 3− -PW 12 O 40 3− mixed layer on a 1:1 GeMo 12 O 40 4− -SiMo 12 O 40 4− mixed layer, which resulted in an 11X capacitance enhancement over unmodified CNT. This dual layer electrode also demonstrated a close to rectangular CV profile due to the overlapping redox features of the POM combination (Figure 1). In this talk we will discuss the unique electrochemistry of various POM mixtures, the mechanisms of the novel mixed addenda synthesis, and our approach for using these molecules to design high performance composite EC electrodes. Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".